phd programs machine learning

Machine Learning (Ph.D.)

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

College of Computing

Ph.d. in machine learning, about the curriculum.

The central goal of the Ph.D. program is to train students to perform original, independent research. The most important part of the curriculum is the successful defense of a Ph.D. dissertation, which demonstrates this research ability.

The curriculum is designed with the following principal educational goals:

•    Students will develop a solid understanding of fundamental principles across a range of core areas in the machine learning discipline. •    Students will develop a deep understanding and set of skills and expertise in a specific theoretical aspect or application area of the machine learning discipline. •    The students will be able to apply and integrate the knowledge and skills they have developed and demonstrate their expertise and proficiency in an application area of practical importance. •    Students will be able to engage in multidisciplinary activities by being able to communicate complex ideas in their area of expertise to individuals in other fields, be able to understand complex ideas and concepts from other disciplines, and be able to incorporate these concepts into their own work. The curriculum for the Ph.D. in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech:  •    Computer Science (Computing) •    Computational Science and Engineering (Computing) •    Interactive Computing (Computing) – see Computer Science •     Aerospace Engineering (Engineering) •     Biomedical Engineering (Engineering) •     Electrical and Computer Engineering (Engineering) •     Industrial Systems Engineering (Engineering) •     Mathematics (Sciences) Students must complete four core courses, five electives, a qualifying exam, and a doctoral dissertation defense. All doctorate students are advised by ML Ph.D. Program Faculty . All coursework must be completed before the Ph.D. proposal. An overall GPA of 3.0 is required for the Ph.D. coursework.

Research Opportunities

Our faculty comes from all six colleges across Georgia Tech’s campus, creating many interdisciplinary research opportunities for our students. Our labs focus on research areas such as artificial intelligence, data science, computer vision, natural language processing, optimization, machine learning theory, forecasting, robotics, computational biology, fintech, and more.

External applications are only accepted for the Fall semester each year. The application deadline varies by home school. 

The Machine Learning Ph.D. admissions process works bottom-up through the home schools. Admissions decisions are made by the home school, and then submitted to the Machine Learning Faculty Advisory Committee (FAC) for final approval. Support for incoming students (including guarantees of teaching assistantships and/or fellowships) is determined by the home schools. 

After the admissions have been approved by the FAC, the home school will communicate the acceptance to the prospective student. The home school will also communicate all rejections.

Get to Know Current ML@GT Students

Learn more about our current students, their interests inside and outside of the lab, favorite study spots, and more.

Career Outlook

The machine learning doctorate degree prepares students for a variety of positions in industry, government, and academia. These positions include research, development, product managers, and entrepreneurs. 

Graduates are well prepared for position in industry in areas such as internet companies, robotic and manufacturing companies and financial engineering, to mention a few. Positions in government and with government contractors in software and systems are also possible career paths for program graduates. Graduates are also well-suited for positions in academia involving research and education in departments concerned with the development and application of data-driven models in engineering, the sciences, and computing. 

Frequently Asked Questions

For additional questions regarding the ML Ph.D. program, please take a look at our frequently asked questions.

You can also view the ML Handbook which has detailed information on the program and requirements.

From the Catalog:

Doctor of Philosophy with a major in Machine Learning

The Doctor of Philosophy with a major in Machine Learning program has the following principal objectives, each of which supports an aspect of the Institute’s mission:

• Create students that are able to advance the state of knowledge and practice in machine learning through innovative research contributions.
• Create students who are able to integrate and apply principles from computing, statistics, optimization, engineering, mathematics and science to innovate, and create machine learning models and apply them to solve important real-world data intensive problems.
• Create students who are able to participate in multidisciplinary teams that include individuals whose primary background is in statistics, optimization, engineering, mathematics and science.
• Provide a high quality education that prepares individuals for careers in industry, government (e.g., national laboratories), and academia, both in terms of knowledge, computational (e.g., software development) skills, and mathematical modeling skills.
• Foster multidisciplinary collaboration among researchers and educators in areas such as computer science, statistics, optimization, engineering, social science, and computational biology.
• Foster economic development in the state of Georgia.
• Advance Georgia Tech’s position of academic leadership by attracting high quality students who would not otherwise apply to Tech for graduate study.

All PhD programs must incorporate a standard set of Requirements for the Doctoral Degree .

The central goal of the PhD program is to train students to perform original, independent research.  The most important part of the curriculum is the successful defense of a PhD Dissertation, which demonstrates this research ability.  The academic requirements are designed in service of this goal.

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in nine schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Aerospace Engineering, Chemical and Biomolecular Engineering, Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

Summary of General Requirements for a PhD in Machine Learning

• Core curriculum (4 courses, 12 hours). Machine Learning PhD students will be required to complete courses in four different areas: Mathematical Foundations, Probabilistic and Statistical Methods in Machine Learning, ML Theory and Methods, and Optimization.   
• Area electives (5 courses, 15 hours).
• Responsible Conduct of Research (RCR) (1 course, 1 hour, pass/fail).  Georgia Tech requires that all PhD students complete an RCR requirement that consists of an online component and in-person training. The online component is completed during the student’s first semester enrolled at Georgia Tech.  The in-person training is satisfied by taking PHIL 6000 or their associated academic program’s in-house RCR course.
• Qualifying examination (1 course, 3 hours). This consists of a one-semester independent literature review followed by an oral examination.
• Doctoral minor (2 courses, 6 hours).
• Research Proposal.  The purpose of the proposal is to give the faculty an opportunity to give feedback on the student’s research direction, and to make sure they are developing into able communicators.
• PhD Dissertation.

Almost all of the courses in both the core and elective categories are already taught regularly at Georgia Tech.  However, two core courses (designated in the next section) are being developed specifically for this program.  The proposed outlines for these courses can be found in the Appendix. Students who complete these required courses as part of a master’s program will not need to repeat the courses if they are admitted to the ML PhD program.

Core Courses

Machine Learning PhD students will be required to complete courses in four different areas. With the exception of the Foundations course, each of these area requirements can be satisfied using existing courses from the College of Computing or Schools of ECE, ISyE, and Mathematics.

Machine Learning core:

Mathematical Foundations of Machine Learning. This required course is the gateway into the program, and covers the key subjects from applied mathematics needed for a rigorous graduate program in ML. Particular emphasis will be put on advanced concepts in linear algebra and probabilistic modeling. This course is cross-listed between CS, CSE, ECE, and ISyE.

ECE 7750 / ISYE 7750 / CS 7750 / CSE 7750 Mathematical Foundations of Machine Learning

Probabilistic and Statistical Methods in Machine Learning

• ISYE 6412 , Theoretical Statistics
• ECE 7751 / ISYE 7751 / CS 7751 / CSE 7751 Probabilistic Graphical Models
• MATH 7251 High Dimension Probability
• MATH 7252 High Dimension Statistics

Machine Learning: Theory and Methods.   This course serves as an introduction to the foundational problems, algorithms, and modeling techniques in machine learning.  Each of the courses listed below treats roughly the same material using a mix of applied mathematics and computer science, and each has a different balance between the two. 

• CS 7545 Machine Learning Theory and Methods
• CS 7616 , Pattern Recognition
• CSE 6740 / ISYE 6740 , Computational Data Analysis
• ECE 6254 , Statistical Machine Learning
• ECE 6273 , Methods of Pattern Recognition with Applications to Voice

Optimization.   Optimization plays a crucial role in both developing new machine learning algorithms and analyzing their performance.  The three courses below all provide a rigorous introduction to this topic; each emphasizes different material and provides a unique balance of mathematics and algorithms.

• ECE 8823 , Convex Optimization: Theory, Algorithms, and Applications
• ISYE 6661 , Linear Optimization
• ISYE 6663 , Nonlinear Optimization
• ISYE 7683 , Advanced Nonlinear Programming

After core requirements are satisfied, all courses listed in the core not already taken can be used as (appropriately classified) electives.

In addition to meeting the core area requirements, each student is required to complete five elective courses. These courses are required for getting a complete breadth in ML. These courses must be chosen from at least two of the five subject areas listed below. In addition, students can use up to six special problems research hours to satisfy this requirement. 

i. Statistics and Applied Probability : To build breadth and depth in the areas of statistics and probability as applied to ML.

• AE 6505 , Kalman Filtering
• BMED 6700 , Biostatistics
• ECE 6558 , Stochastic Systems
• ECE 6601 , Random Processes
• ECE 6605 , Information Theory
• ISYE 6404 , Nonparametric Data Analysis
• ISYE 6413 , Design and Analysis of Experiments
• ISYE 6414 , Regression Analysis
• ISYE 6416 , Computational Statistics
• ISYE 6420 , Bayesian Statistics
• ISYE 6761 , Stochastic Processes I
• ISYE 6762 , Stochastic Processes II
• ISYE 7400 , Adv Design-Experiments
• ISYE 7401 , Adv Statistical Modeling
• ISYE 7405 , Multivariate Data Analysis
• ISYE 8803 , Statistical and Probabilistic Methods for Data Science
• ISYE 8813 , Special Topics in Data Science
• MATH 6263 , Testing Statistical Hypotheses
• MATH 6266 , Statistical Linear Modeling
• MATH 6267 , Multivariate Statistical Analysis
• MATH 7244 , Stochastic Processes and Stochastic Calculus I
• MATH 7245 , Stochastic Processes and Stochastic Calculus II

ii. Advanced Theory: To build a deeper understanding of foundations of ML.

• CS 7280 , Network Science
• CS 7510 , Graph Algorithms
• CS 7520 , Approximation Algorithms
• CS 7530 , Randomized Algorithms
• CS 7535 , Markov Chain Monte Carlo Algorithms
• CS 7540 , Spectral Algorithms
• CS 8803 , Continuous Algorithms
• ECE 6283 , Harmonic Analysis and Signal Processing
• ECE 6555 , Linear Estimation
• ISYE 7682 , Convexity
• MATH 6112 , Advanced Linear Algebra
• MATH 6221 , Advanced Classical Probability Theory
• MATH 6241 , Probability I
• MATH 6580 , Introduction to Hilbert Space
• MATH 7338 , Functional Analysis
• MATH 7586 , Tensor Analysis
• MATH 88XX, Special Topics: High Dimensional Probability and Statistics

iii. Applications: To develop a breadth and depth in variety of applications domains impacted by/with ML.

• AE 6373 , Advanced Design Methods
• AE 8803 , Machine Learning for Control Systems
• AE 8803 , Nonlinear Stochastic Optimal Control
• BMED 6780 , Medical Image Processing
• BMED 8813 BHI, Biomedical and Health Informatics
• BMED 8813 MHI, mHealth Informatics
• BMED 8813 MLB, Machine Learning in Biomedicine
• BMED 8823 ALG, OMICS Data and Bioinformatics Algorithms
• CS 6440 , Introduction to Health Informatics
• CS 6465 , Computational Journalism
• CS 6471 , Computational Social Science
• CS 6474 , Social Computing
• CS 6475 , Computational Photography
• CS 6476 , Computer Vision
• CS 6601 , Artificial Intelligence
• CS 7450 , Information Visualization
• CS 7476 , Advanced Computer Vision
• CS 7630 , Autonomous Robots
• CS 7632 , Game AI
• CS 7636 , Computational Perception
• CS 7643 , Deep Learning
• CS 7646 , Machine Learning for Trading
• CS 7650 , Natural Language Processing
• CSE 6141 , Massive Graph Analysis
• CSE 6240 , Web Search and Text Mining
• CSE 6242 , Data and Visual Analytics
• CSE 6301 , Algorithms in Bioinformatics and Computational Biology
• ECE 4580 , Computational Computer Vision
• ECE 6255 , Digital Processing of Speech Signals
• ECE 6258 , Digital Image Processing
• ECE 6260 , Data Compression and Modeling
• ECE 6273 , Methods of Pattern Recognition with Application to Voice
• ECE 6550 , Linear Systems and Controls
• ECE 8813 , Network Security
• ISYE 6421 , Biostatistics
• ISYE 6810 , Systems Monitoring and Prognosis
• ISYE 7201 , Production Systems
• ISYE 7204 , Info Prod & Ser Sys
• ISYE 7203 , Logistics Systems
• ISYE 8813 , Supply Chain Inventory Theory
• HS 6000 , Healthcare Delivery
• MATH 6759 , Stochastic Processes in Finance
• MATH 6783 , Financial Data Analysis

iv. Computing and Optimization: To provide more breadth and foundation in areas of math, optimization and computation for ML.

• CS 6515 , Introduction to Graduate Algorithms
• CS 6550 , Design and Analysis of Algorithms
• CSE 6140 , Computational Science and Engineering Algorithms
• CSE 6643 , Numerical Linear Algebra
• CSE 6644 , Iterative Methods for Systems of Equations
• CSE 6710 , Numerical Methods I
• CSE 6711 , Numerical Methods II
• ISYE 6644 , Simulation
• ISYE 6645 , Monte Carlo Methods
• ISYE 6662 , Discrete Optimization
• ISYE 6664 , Stochastic Optimization
• ISYE 6679 , Computational methods for optimization
• ISYE 7686 , Advanced Combinatorial Optimization
• ISYE 7687 , Advanced Integer Programming

v. Platforms : To provide breadth and depth in computing platforms that support ML and Computation.

• CS 6421 , Temporal, Spatial, and Active Databases
• CS 6430 , Parallel and Distributed Databases
• CS 6290 , High-Performance Computer Architecture
• CSE 6220 , High Performance Computing
• CSE 6230 , High Performance Parallel Computing

Qualifying Examination

The purpose of the Qualifying Examination is to judge the candidate’s potential as an independent researcher.

The Ph.D. qualifying exam consists of a focused literature review that will take place over the course of one semester.  At the beginning of the second semester of their second year, a qualifying committee consisting of three members of the ML faculty will assign, in consultation with the student and the student’s advisor, a course of study consisting of influential papers, books, or other intellectual artifacts relevant to the student’s research interests.  The student’s focus area and current research efforts (and related portfolio) will be considered in defining the course of study.

At the end of the semester, the student will submit a written summary of each artifact which highlights their understanding of the importance (and weaknesses) of the work in question and the relationship of this work to their current research.  Subsequently, the student will have a closed oral exam with the three members of the committee.  The exam will be interactive, with the student and the committee discussing and criticizing each work and posing questions related the students current research to determine the breadth of student’s knowledge in that specific area.  

The success of the examination will be determined by the committee’s qualitative assessment of the student’s understanding of the theory, methods, and ultimate impact of the assigned syllabus.

The student will be given a passing grade for meeting the requirements of the committee in both the written and the oral part. Unsatisfactory performance on either part will require the student to redo the entire qualifying exam in the following semester year. Each student will be allowed only two attempts at the exam.

Students are expected to perform the review by the end of their second year in the program.

Doctoral Dissertation

The primary requirement of the PhD student is to do original and substantial research.  This research is reported for review in the PhD dissertation, and presented at the final defense.  As the first step towards completing a dissertation, the student must prepare and defend a Research Proposal.  The proposal is a document of no more than 20 pages in length that carefully describes the topic of the dissertation, including references to prior work, and any preliminary results to date.  The written proposal is submitted to a committee of three faculty members from the ML PhD program, and is presented in a public seminar shortly thereafter.  The committee members provide feedback on the proposed research directions, comments on the strength of writing and oral presentation skills, and might suggest further courses to solidify the student’s background.  Approval of the Research Proposal by the committee is required at least six months prior to the scheduling of the PhD defense. It is expected that the student complete this proposal requirement no later than their fourth year in the program. The PhD thesis committee consists of five faculty members: the student’s advisor, three additional members from the ML PhD program, and one faculty member external to the ML program.  The committee is charged with approving the written dissertation and administering the final defense.  The defense consists of a public seminar followed by oral examination from the thesis committee.

Doctoral minor (2 courses, 6 hours): 

The minor follows the standard Georgia Tech requirement: 6 hours, preferably outside the student’s home unit, with a GPA in those graduate-level courses of at least 3.0.  The courses for the minor should form a cohesive program of study outside the area of Machine Learning; no ML core or elective courses may be used to fulfill this requirement and must be approved by your thesis advisor and ML Academic Advisor.  Typical programs will consist of three courses two courses from the same school (any school at the Institute) or two courses from the same area of study. 

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Carnegie Mellon University School of Computer Science

Machine learning department.

Ph.D. in Machine Learning

Machine learning is dedicated to furthering scientific understanding of automated learning and to producing the next generation of tools for data analysis and decision-making based on that understanding. The doctoral program in machine learning trains students to become tomorrow's leaders in this rapidly growing area.

Joint Ph.D. in Machine Learning and Public Policy

The Joint Ph.D. Program in Machine Learning and Public Policy is a new program for students to gain the skills necessary to develop state-of-the-art machine learning technologies and apply these technologies to real-world policy issues.

Joint Ph.D. in Neural Computation and Machine Learning

This Ph.D. program trains students in the application of machine learning to neuroscience by combining core elements of the machine learning Ph.D. program and the Ph.D. in neural computation offered by the Center for the Neural Basis of Cognition.

Joint Ph.D. in Statistics and Machine Learning

This joint program prepares students for academic careers in both computer science and statistics departments at top universities. Students in this track will be involved in courses and research from both the Department of Statistics and the Machine Learning Department.

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The PhD in Machine Learning is an interdisciplinary doctoral program spanning three colleges (Computing, Engineering, Sciences).  Students are admitted through one of nine participating home schools:

• Contact SCS
• Contact CSE
• Contact ChBE
• Contact BME
• Contact ECE
• Contact ISYE
• ​​​​​​​ Contact MATH

Application requirements and deadlines follow the same as that of the home unit an applicant is applying through. For example, ML PhD applicants to the ECE home unit follow the same rules as the PhD ECE application requirements and deadlines. 

External applications are only accepted for the Fall semester each year.  The application deadline varies by home school with the earliest deadline of December 1. Most home schools have a final deadline of December 15. Check with home schools above for more specific details. 

Click here for application information and to apply  

Applicants must meet all admissions standards (including requirements on the minimum GPA, minimum GRE/TOEFL scores) of the home unit, which may vary. After an initial review, the unit’s representative of the ML Ph.D. Faculty Advisory Committee (FAC) will submit their candidates for review and the final admission decision will be made by the ML FAC.

Note most home units have made the GRE optional for fall 2023 applications. Contact the home unit at the above links for any specific info. 

The committee’s decision to admit will be based on (1) prior academic performance of the applicant in a B.S. or M.S. program at a recognized institution, including coursework and independent research projects, (2) prior work experience relevant to ML, (3) the applicant’s statement of purpose, and (4) the letters of support.

Please note that application requirements may vary by home unit, including the application deadlines and test score requirements, as well as support for incoming students (including guarantees of teaching assistantships and/or fellowships) are determined by the home units. Please review the home unit links above or contact them directly for details.

Have Questions?

Please contact the above  home units directly for questions related to:.

• Application deadlines
• Application fee waivers
• Assistantship/fellowship opportunities
• Program fit
• Advising Matching
• GRE requirements - Many units have made this test optional. 
• TOEFL minimum requirements and TOEFL waivers are determined by the GT Graduate Education Office:  https://grad.gatech.edu/english-proficiency . Note home units may required higher scores. 
• Desired content in Statement of Purpose and Recommendation Letters

For technical application questions, please contact  [email protected]: 

• Creating or using an account login
• Forgotten password
• Uploading documents 
• Difficulty with recommender emails
• How to access application status information (including application checklist)
• Difficulty with the touchnet payment system

For general inquiries about curriculum or program requirements, please see FAQs or contact [email protected] .

Georgia Tech Transfer Students

If you are already enrolled in a Ph.D. program in one of the nine participating schools noted above, you may apply to the ML Ph.D. program as a transfer student.  You will be subject to the standard ML curriculum and qualifying requirements, so this is recommended only for graduate students in their first or second year.  

Potential transfer students must have a ML PhD Program thesis adviso r  who is willing to support them on a research assistantship. For more information, please email [email protected] .

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Machine Learning and Big Data PhD Track

Optional PhD Tracks:   Statistical Genetics ,  Statistics in the Social Sciences ,  Machine Learning and Big Data

About The UW Department of Statistics now offers a PhD track in the area of Machine Learning and Big Data. All incoming and current students are eligible to apply. The goal of the PhD track is to prepare students to tackle large data analysis tasks with the most advanced tools in existence today, while building a strong methodological foundation. Students in this track will have a multidisciplinary experience, taking courses across departments and interacting with faculty and graduate students from these departments. A similar PhD track is being offered in  Computer Science and Engineering  (CSE), and students from both of these tracks will interact significantly in the core courses.

More details about ML @ UW can be found  here  and  here .

For application details, click  here .

Program Requirements

• Statistics Core:  STAT 570 ,  STAT 581 ,  STAT 582
• ML/BD Core:
• (i) Foundational ML:  STAT 535 (ii) One advanced ML course:  STAT 538  or  STAT 548 (iii) One CSE course:  CSE 544  (Databases) or CSE 512 (Visualization) (iv) One MLBD related elective such as a course from the list below and Two electives from the general electives list:        * Advanced Statistical Learning ( STAT 538 )       * Machine Learning for Big Data ( STAT 548 )       * Graphical Models ( CSE 515 )       * Visualization (CSE 512)       * Databases ( CSE 544 )       * Convex Optimization ( EE 578 )
• All other statistics PhD requirements hold, except  STAT 571  may be used to satisfy the Applied Data Analysis Project.
• STAT 583 is not required.

Advanced Data Science Transcriptable Option A student in the MLBD track can, in addition, choose to enroll/satisfy the Advanced Data Science Option. To further expand students' education and create a campus-wide community, students will register for at least 4 quarters in the weekly  eScience Community Seminar . Satisfying this option means that the student will have "ADS" listed on their transcript.

• eScience ADSO

ML Lunch Series A lunchtime seminar on a topic related to machine learning is held nearly weekly on Tuesdays during term. Lunch is provided. Updates are posted  here .

ML Mailing List General announcements related to machine learning are made on the  ML mailing list .

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Best Online Doctorates in Machine Learning: Top PhD Programs, Career Paths, and Salary

Machine learning is a rapidly growing, fascinating field dealing with algorithm development that can be used to make predictions from data. The best online PhD in Machine Learning prepares students for a career in this promising field.

The best online doctorates in machine learning offer students a comprehensive education in all aspects of the field. Students are also provided with the opportunity to choose a specialization such as deep learning, natural language processing , or computer vision. Find out in this article what machine learning PhD online degree program best fits you and the machine learning jobs for graduates.

Find your bootcamp match

Can you get a phd in machine learning online.

Yes, you can get a PhD in Machine Learning online. The online learning system has seen rapid growth in many academic fields and has given students the opportunity to virtually access the academic curriculum remotely.

Many online PhD programs in the United States are accredited and designed with working professionals in mind. Online learning is a great way to earn a doctorate without sacrificing your day job, and in most cases, online students can complete their entire academic journey without stepping foot on campus.

Is an Online PhD Respected?

Yes, an online PhD is respected when it is obtained from an accredited institution in the US. A PhD from an unaccredited school is regarded as just an expensive piece of paper by many other academic institutions.

In regard to employment, many companies and organizations respect an online PhD, holding it to the same standard as an in-person PhD. However, some employers prefer in-person degrees and will disregard online degrees. Ensure your potential future employer accepts online degrees as credible education.

What Is the Best Online PhD Program in Machine Learning?

The best online PhD program in machine learning is at Clarkson University in Potsdam, New York. It is regionally accredited by the Middle States Commission on Higher Education and has an excellent reputation within the academic community, a student-to-faculty ratio of 12 to one, and one in five of its 44,000 alumni is a CEO or executive.

Why Clarkson University Has the Best Online PhD Program in Machine Learning

Clarkson University has the best machine learning PhD program not only because it is accredited by the Middle States Commission on Higher Education (MSCHE) but also because of its US News & World Report ranking. MSCHE is a regionally recognized accreditation association that uses a rigorous and comprehensive system for the purpose of accreditation.

Referring to US News & World Report, Clarkson University is ranked 127 for best national universities out of 4000 degree-granting academic institutions in the United States and 49 for best value schools.

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Online PhD in Machine Learning Admission Requirements

The admission requirements for an online PhD in Machine Learning typically include a master’s degree or Bachelor’s in Machine Learning or a related subject like the field of engineering. Moreover, prepare to submit official transcripts from previously attended postsecondary institutions and GRE test scores.

Additionally, you may be asked to submit three letters of recommendation, a statement of purpose, a CV or resume, and prove your knowledge of calculus and your fluency in computer programming languages like Python and Java. Below is a list of the typical admission requirements needed by distinct schools that offer a machine learning PhD program.

• Master’s or bachelor’s degree in a relevant field
• Official transcripts and GRE test scores
• Letters of recommendation
• Statement of purpose
• CV or resume
• Knowledge of programming and calculus

Best Online PhDs in Machine Learning: Top Degree Program Details

Best online phds in machine learning: top university programs to get a phd in machine learning online.

The top university programs to get a PhD in Machine Learning are at Clarkson University, Aspen University, Capitol Technology University, The University of Rhode Island, and The University of the Cumberlands, among other distinct schools.

This section discusses the properties, requirements, and descriptions of the best universities offering online PhD in Machine Learning programs. We have created this list below to narrow down your school search for these graduate-level in-depth study programs.

Aspen University is a Distance Education Accrediting Commission accredited university. It was established in 1987 as a private for-profit online university offering undergraduate and graduate degrees in computer science, business information systems, and project management.

Aspen University in Phoenix, Arizona is a known member of the Council for Adult and Experiential Learning and is dedicated to supporting adult learners in achieving a professional career in whatever field they desire.

DSc in Computer Science

This doctoral degree teaches students the theory and practical application of computer science in data science, application design, and computer architecture. It contains 20 courses, including artificial intelligence, risk analysis, and system metrics. 

These courses are offered online and aim to impart students with the necessary skills for improving existing technology, as well as evaluating and applying them. It also contains courses that aid doctoral students in carrying out their research dissertations.

DSc in Computer Science Overview

• Accreditation: Distance Education Accrediting Commission
• Program Length: 5 years and 7 months
• Acceptance Rate: N/A
• Tuition and Fees: $375/month

DSc in Computer Science Admission Requirements

• Master’s degree
• Statement of goals
• Minimum of 3.0 GPA
• Must know about object-oriented development

Capitol Technology University was founded in 1927 and offers online programs at the undergraduate, graduate, and doctoral levels. The areas of study in which these online programs are offered include business, technology, and the field of engineering.

PhD in Artificial Intelligence

This is a research-based PhD program that offers students the opportunity to conduct research in any field of their choice. Throughout the program, student work must be approved by the academic supervisor. Students are to submit a thesis and give an oral presentation which will be supervised by an expert in the field.

PhD in Artificial Intelligence Overview

• Accreditation: Middle States Commission on Higher Education
• Program Length: 2 to 3 years
• Tuition and Fees: $933/credit

PhD in Artificial Intelligence Admission Requirements

• Application fee of $100
• Master’s degree in a relevant field
• Minimum of five years of related work experience
• Two recommendation letters

Founded in 1973, City University of Seattle is recognized as a top 10 educator of adults nationwide, as ranked by the US News & World Report for school rankings. It offers online undergraduate, graduate, and doctoral programs designed for working professionals

PhD in Information Technology

The program’s curriculum consists of courses in machine and deep learning. Candidates are given the option to propose their depth of study, which requires approval from the academic director. The program consists of core courses, concentration courses, a comprehensive examination, a research core, and a dissertation. 

PhD in Information Technology Overview

• Accreditation: Northwest Commission on Colleges and Universities
• Program Length: Flexible
• Acceptance Rate: 100% due to open admission policy
• Tuition and Fees: $765/credit

PhD in Information Technology Admission Requirements

• A master’s degree from an accredited or recognized institution
• CV and resume, and three references letters 
• Proof of English proficiency
• Interview with admissions advisor
• State goals related to your academic work

Founded in 1896 to honor Thomas S. Clarkson, Clarkson University offers flexible online degree programs at the undergraduate and graduate levels. It is a research university that leads in technology education. 

PhD in Computer Science

This doctoral program places emphasis on areas such as artificial intelligence , software, security, and networking. Current students are required to complete 36 credits of computer science foundation and research-oriented courses, elective courses, achieve candidacy within the first two years of the program, and propose and defend a thesis.

PhD in Computer Science Overview

• Program Length: 3 years
• Tuition and Fees: $1,533/credit

PhD in Computer Science Admission Requirements

• Complete the online application form
• Resume, statement of purpose, and three letters of recommendation
• English proficiency test for international applicants (TOEFL, IELTS, PTE, and Duolingo English Test)

Northcentral University is a private university established in 1996 and is designed for flexibility by offering programs of distance learning for working professionals. It practices a distinctive one-to-one learning system and has a dedicated doctoral faculty.

In this doctorate program, besides writing papers about past research, students are allowed to propose their research. Its curriculum consists of subjects such as software engineering , artificial intelligence, data mining, and cyber security. Through the course, students conduct research and examine real-world issues in the field of computer science.

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• Accreditation: WASC Senior College and University Commission
• Program Length: 3 years and 4 months
• Tuition and Fees: $1,094/credit
• Master’s degree from an accredited institution
• Official transcripts
• English proficiency exam score for international students

Nova Southeastern University was founded in 1964 in Fort Lauderdale, Florida. It offers online graduate and undergraduate courses and conducts a wide variety of interdisciplinary healthcare research. It is home to national athletics champions and Olympians.

This program provides research in computer science. Its format of learning combines both traditional and online instruction designed with consideration for working professionals . Its coursework consists of research in computer science areas, including cyber security, software engineering, and artificial intelligence.

• Accreditation: Southern Association of Colleges and Schools, Commission on Colleges
• Program Length: Not specified
• Tuition and Fees: $1,282/credit
• Online application and $50 application fee
• A bachelor’s or master’s degree in a relevant field from a regionally accredited institution
• GPA of at least 3.20 
• Official transcripts from all institutions attended 
• A resume  
• Essay, and three letters of recommendation

The University of North Dakota was founded in 1883, six years before North Dakota was made a state. Today, it offers several academic programs in undergraduate, graduate, and doctoral fields and is known for conducting research in areas that include medicine, aerospace, and engineering.

This PhD in Computer Science curriculum consists of courses in machine learning, software engineering, applications of AI, computer forensics, and computer networks which benefit students by granting them proficiencies in areas such as artificial intelligence, compiler design, operating systems, simulation, databases, and networks.

• Accreditation: Higher Learning Commission
• Program Length: 4 to 5 years
• Tuition and Fees: $545.16/credit (in state); $817.73/ credit (out of state)
• Application fee of $35
• Master’s or bachelor’s degree in engineering or a related science field
• GPA of 3.0 on a 4.0 scale and GRE test score
• Official copy of all college and university academic transcripts
• Statement of academic goals and three letters of recommendation
• Expertise in a high-level programming language and basic knowledge of data structures, formal languages, computer architecture and OS, calculus, statistics, and linear algebra 
• English language proficiency

The University of Rhode Island is a public research institution founded in 1892. It conducts extensive research in the field of science. It offers online, on-site, and hybrid programs at the graduate and undergraduate levels, as well as certificate programs.

In this PhD in Computer Science program, students are involved in research geared toward producing new intellectual and innovative contributions to the field of computer science. It offers a combination of on-campus, online, and day and evening classes. It consists of courses in machine learning, artificial intelligence, software engineering, and systems simulation.

• Accreditation: New England Commission of Higher Education
• Program Length: 4 years
• Tuition and Fees: $14,454/year (in-state); $27,906/ year (out of state)
• An undergraduate degree from a regionally accredited institution in the US
• A minimum GPA of 3.0
• All official college transcripts
• Personal statement
• An application fee of $65

Founded in 1888 by Baptist ministers in Williamsburg KY, today the University of the Cumberlands offers online master's and doctoral degree programs in the fields of education, information technology, and business.

The program requires 18 credit hours of core courses which include information technology geared toward creating machine learning engineers . Its curriculum focuses on predictive analytics and other skills students need to become experts in cyber crime security, big data, and smart technologies.

Students have the option to specialize in information systems security, information technology, digital forensics, or blockchain technologies. Students will complete 21 credit hours of professional research while working toward a dissertation.

• Tuition and Fees: $500/credit
• A master’s degree from a regionally accredited institution
• TOEFL for non-native English speakers
• Application fee of $30

Wright State University was first seen in 1964 as a branch campus for Ohio State University and Miami University. It is a Carnegie classified research university and offers research at the undergraduate, graduate, and doctoral levels.

PhD in Computer Science and Engineering

This degree is awarded to students who show excellence in study and research that significantly contributes to the field of computer science and engineering. The degree requirements include an A grade completion of the core coursework in two areas and at least a B in the third. 

Students are to complete a minimum of 18 hours of residency research before taking the candidacy exam, which must be completed with a satisfactory grade. Also, a minimum of 12 hours of dissertation research is needed before the dissertation defense, which has to be approved.

PhD in Computer Science and Engineering Overview

• Program Length: 10 years time limit
• Tuition and Fees: $660/credit (in state); $1,125/ credit (out of state)
• Bachelor’s or master’s degree in a related discipline (computer science or engineering)
• Minimum GPA of 3.0 if admitted with a bachelor’s degree or 3.3 with a master’s degree
• GRE general test portion
• TOEFL score for non-native English speakers
• Knowledge of high-level programming languages, computer organization, operating systems, data structures, and computer systems design
• A record that indicates potential for a career in research

Online Machine Learning PhD Graduation Rates: How Hard Is It to Complete an Online PhD Program in Machine Learning?

It is very hard to complete an online PhD in Machine Learning. According to a paper published in the International Journal of Doctoral Studies, there is a PhD attrition rate of 50 percent in the US within the past 50 years. Therefore, the graduation rate for doctorate students is approximately 50 percent.

How Long Does It Take to Get a PhD in Machine Learning Online?

It takes about four years to get a PhD in Machine Learning online, which is fast when compared to a traditional in-person PhD program which may take over seven years to complete. Online PhD programs are accelerated by default, so the curriculum focuses on the major needs of a PhD graduate in the areas of research, thesis, and dissertation.

Students may be able to reduce the time spent pursuing a PhD in Machine Learning by first acquiring a master’s degree in the field. If you choose to pursue a PhD on a part-time schedule as opposed to full-time study, it will significantly increase the time it takes to acquire the degree.

How Hard Is an Online Doctorate in Machine Learning?

Getting an online doctorate in machine learning is very hard, as are most graduate programs. Besides the rigorous research, strict requirements, deadlines, qualification examinations, and dissertations, other challenges may exist, such as limited student connection with the faculty members, isolation, financial issues, and lack of an adequate work-life balance .

Getting a doctorate in any field is not easy. In fact, there is research to suggest that online doctorate students face challenges regarding culture and academia. As a result of these challenges, many students drop out from their PhD programs.

Best PhD Programs

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What Courses Are in an Online Machine Learning PhD Program?

The courses in an online machine learning PhD program include an introduction to machine learning and deep learning, artificial intelligence, statistical theories, data mining , system simulation, computer programming, and software development.

Main Areas of Study in a Machine Learning PhD Program

• Machine learning
• Deep learning
• Artificial intelligence
• Databases and data mining
• Statistical theory
• Software engineering
• Systems simulation

How Much Does Getting an Online Machine Learning PhD Cost?

On average, it costs $19,314 per year to get a PhD in Machine Learning, according to the National Center of Education Statistics (NCES). However, this figure is not fixed, as the total tuition for a PhD program varies from school to school.

Private institutions generally cost more than public institutions, but there are funding opportunities for PhD students. Some PhD programs may guarantee financial aid for all their students regardless of merit.

How to Pay for an Online PhD Program in Machine Learning

You can pay for an online PhD in Machine Learning by taking advantage of student loans, scholarships, grants, teaching and research assistantships, graduate assistantships, and fellowship assistantships. As a result, most PhD students spend less than the tuition fee displayed on a school’s website.

How to Get an Online PhD for Free

You cannot get an online PhD in Machine Learning for free. However, there are ways to reduce the cost, or get partial tuition discounts and stipends through graduate assistantships, fellowships, scholarships, or grants.

What Is the Most Affordable Online PhD in Machine Learning Degree Program?

The most affordable online PhD in Machine Learning based on cost per credit is at Aspen University in Phoenix, Arizona. It charges $375 per month, which, when multiplied by the 67 months it takes to complete the program, results in a total of $25,125 for the entire program. This is more affordable compared to a school like Clarkson University, which charges $1,533 per credit hour.

Most Affordable Online PhD Programs in Machine Learning: In Brief

Why you should get an online phd in machine learning.

You should get an online PhD in Machine Learning because having a PhD offers you a stronger advantage in terms of employability, salary, and in your career in general that would otherwise be unavailable with just a bachelor’s and master’s degree.

Top Reasons for Getting a PhD in Machine Learning

• Research opportunities. PhD students get the opportunity to be involved in rigorous and innovative research that may positively impact humanity, add to the world’s knowledge, and improve the lives of others.
• Expertise development. A PhD is the highest level of academic degree, and as a result, PhD holders have expert-level knowledge in whichever field they acquire a PhD in. However, it is advised to only get a PhD if you are very interested in the field and willing to explore your interest and expand your understanding through cutting-edge research.
• Access to better jobs. There are lots of bachelor’s and master’s degree graduates in the job market, and earning a PhD will help you stick out from the crowd. A PhD reveals career opportunities that may not be available to bachelor’s and master’s degree grads.
• Networking opportunities . During a PhD program, students are in contact with top lecturers and academic experts by attending guest lectures, conferences, seminars, and workshops. Students can network with colleagues and classmates, which helps put them in a good position after their academic journey.

Best Master’s Degree Programs

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What Is the Difference Between an On-Campus Machine Learning PhD and an Online PhD in Machine Learning?

The difference between an on-campus machine learning PhD and an online PhD in Machine Learning is primarily the mode of learning. Online PhDs are as rigorous and effective as their on-campus counterparts.

However, there may be some slight differences between the two in terms of cost, schedule, quality, and funding. Some of the differences that may exist are discussed below.

Online PhD vs On-Campus PhD: Key Differences

• Affordability. An online PhD is more affordable compared to the traditional on-campus alternative. An on-campus PhD can cost as much as $30,000 per year, while an online PhD may be as low as $20,000 per year.
• Flexibility. Online PhD students have the liberty to conduct in-depth study and research at their own time as opposed to the schedule of an in-person PhD program. Moreover, most online PhD programs don’t have an enrollment date, and some online PhD work is asynchronous, meaning students can take classes from anywhere at their convenience.
• Quality. Traditionally acquired PhDs are thought to be superior to their online counterparts by some employers and academics, probably due to sentiment. However, the quality of an online PhD is dependent on the research subject, the school’s reputation, and accreditation.
• Availability of funding. Funding available for online PhD programs may be limited due to some geographical constraints. For example, online PhD students cannot take up teaching assistantship positions unless they are willing to be physically present.

How to Get a PhD in Machine Learning Online: A Step-by-Step Guide

To get a PhD in Machine Learning, you need to first apply online to a PhD program. If accepted, you must enroll in the required classes and complete the academic coursework, research, and a series of academic milestones, which include attaining candidacy, passing the qualification examinations, proposing, writing, and defending your dissertation.

To begin your journey to acquiring a PhD in Machine Learning, you first need to apply online to the school of your choice. You also need to fulfill the admission requirements, including possessing a master's or bachelor's degree–depending on the school–in a relevant field, a minimum grade point average, letters of recommendation, and GRE test scores . 

Many online PhD programs require students to take and pass a minimum number of credit hours in core and elective courses. A typical online PhD in Machine Learning program consists of about 70 to 90 credit hours that involve intensive research in a provided or chosen area of concentration. 

Obtaining a PhD in Machine Learning allows an individual to become a world-renowned expert in the field. After completing a rigorous course of study and passing a series of exams, the doctoral candidate would then undertake an original research project that contributes new knowledge to the field. Upon successful completion of the degree, the graduate would be able to pursue a career in academia or industry. 

Examinations are an essential part of any education. They test a student's understanding of the material and help them to learn and remember the information. If you want to earn a machine learning PhD, you must pass the examinations for various core and required courses. Then, you will need to complete and defend your dissertation.

A dissertation is a research paper that is submitted to and defended by a graduate student to earn a graduate degree. To graduate with a PhD in Machine Learning, you are required to write a dissertation on a topic related to machine learning. Your doctoral dissertation must demonstrate your knowledge and understanding of the field of machine learning, as well as your ability to conduct original research in the field.

Online PhD in Machine Learning Salary and Job Outlook

The job outlook for machine learning jobs is 22 percent between 2020 and 2030 , with the number of new jobs expected in this time frame being 7,200, according to the US Bureau of Labor Statistics. The average salary for computer and information research scientists, which is a category that machine learning professionals belong to, is $131,490 per year .

What Can You Do With an Online Doctorate in Machine Learning?

With an online doctorate in machine learning, you can qualify for specialization roles and lead machine learning positions, including senior machine learning engineer and computer research scientist.

Depending on your preferences, you may also opt for a research and academic career path to become a university professor. The list below is a list of the best jobs for PhD in Machine Learning graduates.

Best Jobs with a PhD in Machine Learning

• Senior Machine Learning Engineer
• Computer and Information Research Scientist
• Data Scientist
• Software Engineer
• Postsecondary Teacher

Potential Careers With a Machine Learning Degree

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What Is the Average Salary for an Online PhD Holder in Machine Learning? 

The average salary for a PhD in Machine Learning holder is $108,000 per year , according to PayScale’s salary for skills in machine learning. The average salary a PhD holder receives depends on the location and position you apply for.

Highest-Paying Machine Learning Jobs for PhD Grads

Best machine learning jobs for online phd holders.

The best machine learning jobs for online PhD holders are typically high-paying jobs that require advanced-level skills that coincide with the nature of the position they undertake. Below are some typical job titles that online machine learning PhD degree holders assume.

A senior machine learning engineer oversees a team of machine engineers charged with designing and developing effective machine learning and deep learning solutions implemented in machine learning systems.

• Salary with a Machine Learning PhD: $153,255
• Job Outlook: 22% job growth from 2020 to 2030
• Number of Jobs: 33,000
• Highest-Paying States: Oregon, Arizona, Texas

Computer and information research scientists research and develop new ways of solving complex computing problems and apply existing technology. They work to significantly increase the knowledge in the field of computer science, which will aid in the production of more efficient software and hardware technologies.

• Salary with a Machine Learning PhD: $131,490

A senior data scientist is responsible for developing data mining and machine learning techniques to solve complex business problems. They identify patterns and trends in large data sets, develop models to improve forecasting and decision making, and effectively communicate data-driven insights to non-technical stakeholders and lead a team of data analysts.

• Salary with a Machine Learning PhD: $127,455

A software engineer is a professional that develops and maintains software. They work on a variety of software, from operating systems to video games, and may be involved in the development of websites. They must also have an excellent understanding of computer programming languages and be able to solve complex problems.

• Salary with a Machine Learning PhD: $121,115
• Number of Jobs: 1,847,900
• Highest-Paying States: Washington, California, New York

Postsecondary teachers are in charge of lecturing students in colleges and universities. They are also responsible for instructing adults in several academic and non-academic subjects including career, work, and research.

• Salary with a Machine Learning PhD: $79,640
• Job Outlook: 12% job growth from 2020 to 2030
• Number of Jobs: 1,276,900
• Highest-Paying States: California, Oregon, District of Columbia

Is It Worth It to Do a PhD in Machine Learning Online?

Yes, it is worth it to do a PhD in Machine Learning online. Getting a PhD is not for everyone, as the process will require tremendous effort and discipline, but it can be rewarding. A PhD in Machine Learning online allows you to learn from some of the best minds in the field.

You can also specialize in an area of your choice, such as big data, natural language processing, or deep learning. Specializing in one area for your PhD in Machine Learning allows you to deep-dive into that subject and build doctorate-level expertise.

An online PhD in Machine Learning provides students with the same high-quality education as a traditional PhD but with more flexibility and affordability. You’ll have access to top-notch instructors, state-of-the-art technology, and a thriving online community of experts.

Additional Reading About Machine Learning

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Online PhD in Machine Learning FAQ

Yes, you should get an online PhD in Machine Learning if it is critical for your career prospects. An online PhD in Machine Learning allows you to learn at your own pace and keep your day job while you pursue your degree. In the end, it sets you up for the highest-earning jobs in the machine learning industry , with better pay and a larger professional network.

The type of research you will carry out as a machine learning student includes research in deep learning, neural networks , machine learning algorithms, supervised and unsupervised machine learning, predictive learning, and computer vision. Students will make use of quantitative and experimental methods of research as well as the use of optimal feature selection.

You can choose a concentration for an online machine learning PhD by factoring in your interests, strengths, and career goals. You may also consider recent trends, the average salary of machine learning professionals , or the career options the machine learning industry has to offer when choosing a machine learning concentration.

Examples of online machine learning PhD dissertations include experimental quantum speed-up in reinforcement learning agents, improving automated medical diagnosis systems with machine learning technologies, regulating deep learning and robotics, and the use of machines and robotics in medical procedures.

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PhD Programme in Advanced Machine Learning

The Cambridge Machine Learning Group (MLG) runs a PhD programme in Advanced Machine Learning. The supervisors are Jose Miguel Hernandez-Lobato , Carl Rasmussen , Richard E. Turner , Adrian Weller , Hong Ge and David Krueger . Zoubin Ghahramani is currently on academic leave and not accepting new students at this time.

We encourage applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. There are no additional restrictions on the topic of the PhD, but for further information on our current research areas, please consult our webpages at http://mlg.eng.cam.ac.uk .

The typical duration of the PhD will be four years.

Applicants must formally apply through the Applicant Portal at the University of Cambridge by the deadline, indicating “PhD in Engineering” as the course (supervisor Hernandez-Lobato, Rasmussen, Turner, Weller, Ge and/or Krueger). Applicants who want to apply for University funding need to reply ‘Yes’ to the question ‘Apply for Cambridge Scholarships’. See http://www.admin.cam.ac.uk/students/gradadmissions/prospec/apply/deadlines.html for details. Note that applications will not be complete until all the required material has been uploaded (including reference letters), and we will not be able to see any applications until that happens.

Gates funding applicants (US or other overseas) need to fill out the dedicated Gates Cambridge Scholarships section later on the form which is sent on to the administrators of Gates funding.

Deadline for PhD Application: noon 5 December, 2023

Applications from outstanding individuals may be considered after this time, but applying later may adversely impact your chances for both admission and funding.

FURTHER INFORMATION ABOUT COMPLETING THE ADMISSIONS FORMS:

The Machine Learning Group is based in the Department of Engineering, not Computer Science.

We will assess your application on three criteria:

1 Academic performance (ensure evidence for strong academic achievement, e.g. position in year, awards, etc.) 2 references (clearly your references will need to be strong; they should also mention evidence of excellence as quotes will be drawn from them) 3 research (detail your research experience, especially that which relates to machine learning)

You will also need to put together a research proposal. We do not offer individual support for this. It is part of the application assessment, i.e. ascertaining whether you can write about a research area in a sensible way and pose interesting questions. It is not a commitment to what you will work on during your PhD. Most often PhD topics crystallise over the first year. The research proposal should be about 2 pages long and can be attached to your application (you can indicate that your proposal is attached in the 1500 character count Research Summary box). This aspect of the application does not carry a huge amount of weight so do not spend a large amount of time on it. Please also attach a recent CV to your application too.

INFORMATION ABOUT THE CAMBRIDGE-TUEBINGEN PROGRAMME:

We also offer a small number of PhDs on the Cambridge-Tuebingen programme. This stream is for specific candidates whose research interests are well-matched to both the machine learning group in Cambridge and the MPI for Intelligent Systems in Tuebingen. For more information about the Cambridge-Tuebingen programme and how to apply see here . IMPORTANT: remember to download your application form before you submit so that you can send a copy to the administrators in Tuebingen directly . Note that the application deadline for the Cambridge-Tuebingen programme is noon, 5th December, 2023, CET.

What background do I need?

An ideal background is a top undergraduate or Masters degree in Mathematics, Physics, Computer Science, or Electrical Engineering. You should be both very strong mathematically and have an intuitive and practical grasp of computation. Successful applicants often have research experience in statistical machine learning. Shortlisted applicants are interviewed.

Do you have funding?

There are a number of funding sources at Cambridge University for PhD students, including for international students. All our students receive partial or full funding for the full three years of the PhD. We do not give preference to “self-funded” students. To be eligible for funding it is important to apply early (see https://www.graduate.study.cam.ac.uk/finance/funding – current deadlines are 10 October for US students, and 1 December for others). Also make sure you tick the box on the application saying you wish to be considered for funding!

If you are applying to the Cambridge-Tuebingen programme, note that this source of funding will not be listed as one of the official funding sources, but if you apply to this programme, please tick the other possible sources of funding if you want to maximise your chances of getting funding from Cambridge.

What is my likelihood of being admitted?

Because we receive so many applications, unfortunately we can’t admit many excellent candidates, even some who have funding. Successful applicants tend to be among the very top students at their institution, have very strong mathematics backgrounds, and references, and have some research experience in statistical machine learning.

Do I have to contact one of the faculty members first or can I apply formally directly?

It is not necessary, but if you have doubts about whether your background is suitable for the programme, or if you have questions about the group, you are welcome to contact one of the faculty members directly. Due to their high email volume you may not receive an immediate response but they will endeavour to get back to you as quickly as possible. It is important to make your official application to Graduate Admissions at Cambridge before the funding deadlines, even if you don’t hear back from us; otherwise we may not be able to consider you.

Do you take Masters students, or part-time PhD students?

We generally don’t admit students for a part-time PhD. We also don’t usually admit students just for a pure-research Masters in machine learning , except for specific programs such as the Churchill and Marshall scholarships. However, please do note that we run a one-year taught Master’s Programme: The MPhil in Machine Learning, and Machine Intelligence . You are welcome to apply directly to this.

What Department / course should I indicate on my application form?

This machine learning group is in the Department of Engineering. The degree you would be applying for is a PhD in Engineering (not Computer Science or Statistics).

How long does a PhD take?

A typical PhD from our group takes 3-4 years. The first year requires students to pass some courses and submit a first-year research report. Students must submit their PhD before the 4th year.

What research topics do you have projects on?

We don’t generally pre-specify projects for students. We prefer to find a research area that suits the student. For a sample of our research, you can check group members’ personal pages or our research publications page.

What are the career prospects for PhD students from your group?

Students and postdocs from the group have moved on to excellent positions both in academia and industry. Have a look at our list of recent alumni on the Machine Learning group webpage . Research expertise in machine learning is in very high demand these days.

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Joint machine learning phd degrees, students interested in a ml- joint phd degree should first apply to the phd program that best aligns with their research interests., phd in statistics & machine learning.

This PhD program differs from the Machine Learning PhD program in that it places significantly more emphasis on preparation in statistical theory and methodology. Similarly, this program differs from the Statistics PhD program in its emphasis on machine learning and computer science. The Joint PhD Program in Machine Learning and Statistics is aimed at preparing students for academic careers in both CS and Statistics departments at top universities or industry.

The student must be advised by a faculty from the home department along with a Core Faculty member from the joint department as a co-mentor. Joint Statistics-MLD faculty cannot serve both roles.  Both faculty must be identified at the time of admission to the joint program.

Note: MLD students are able to apply for this program after they have completed the courses and have a sponsoring faculty in Statistics make the case for admission.

PhD in Machine Learning & Public Policy

The Joint Ph.D. Program in Machine Learning and Public Policy is a program operated jointly by faculty in Machine Learning and the Heinz College (Schools of Public Policy, Information Systems, and Management). Students will gain the skills necessary to develop new state-of-the-art machine learning technologies and apply these successfully to real-world policy domains.

PhD in Neural Computation & Machine Learning

This Joint PhD program trains students in the application of machine learning to neuroscience and neural inspired machine learning algorithms by combining core elements of the ML PhD program and the Program in Neural Computation (PNC) offered by the Neuroscience Institute (NI) .

The MLD requirements for graduation with a Joint-ML PhD degree are the same as those for the regular MLD PhD requirements (including the requirement for the PhD thesis committee composition), with only the following differences:

• A Joint-ML PhD thesis will be a contribution to the combination of Machine Learning and the other field.
• The single elective course, the speaking and writing skills requirements, and the Data Analysis requirement (10718) may be satisfied within the student’s home department.
• A Joint-ML PhD student is still required to TA twice, but only one TA-ship has to be within MLD

A student in a Joint-ML PhD degree may earn a MS degree along the way, either from their home department or from MLD, but not from both.  To earn an MS in Research from MLD they must satisfy all the relevant requirements .

To be considered...

In order to apply to a Joint ML PhD degree, a student must already be enrolled in one of the participating PhD programs in Statistics, PNC or Heinz.

Before applying, a student must:

• Take 10715, 36705, 10716 and earn at least a grade of A- in your first attempt to take each course. Letter grades are required. (Students who took courses before June 2023, will be Grandfathered in under the previous of B+ for the courses already taken.)
• Identify a MLD Core Faculty member who agrees to serve as their MLD mentor . 

Applications must be submitted by May 31st .

How to Apply:

Please submit your online application by May 31st to include the following information:

• Statement of Purpose of why you would like to pursue the joint PhD degree.
• Your updated CV
• Your unofficial Carnegie Mellon Transcript, including your letter grades for 10715, 36705 & 10716.
• Your GRE & TOEFL (if applicable) from your original application to your current PhD program.
• Recommenders: (1) Ask your advisor to send a letter of recommendation with their agreement that the joint program would be a good thing for you to pursue and how it would benefit your research. (2) Ask your ML Core Faculty Mentor to send a letter of recommendation including why you would be a good fit for the joint program.